An event can be detected from time-series data sequences using several techniques known in the art. Generally the time-series data sequences extracted using a sensor comprise unique signal patterns corresponding to the event. For example, the event in case of a vehicle may be an abrupt braking of the vehicle, rapid rise/fall in acceleration/deceleration of the vehicle, change in direction of the vehicle and the like. The time series data sequences may be processed using techniques known in the art, for recognizing the unique signal patterns and further identifying the event based on the unique signal patterns.
Referring to FIG. 1a and FIG. 1b, the time-series data sequences captured using the sensor are illustrated, wherein x-axis represents time in seconds and y-axis represents values of speed. The FIG. 1a shows a time-series data sequence representing speed of the vehicle. A window 102 of the time-series data sequence highlights an event related to the vehicle. The event may be a hard-stop event related to the vehicle. During the hard-stop event while brakes are applied to the vehicle running at high speeds, the speed of the vehicle may reduce drastically. The unique signal pattern present in the time-series data sequence, as illustrated in the window 102, may be identified and thus the hard-stop event related to the vehicle may be determined. FIG. 1b shows a time-series data sequence representing speed of the vehicle in another case. Further, windows 104 highlight the unique signal patterns indicating the hard-stop events related to the vehicle, where speed of the vehicle reduces drastically.